Investigation of a Bridge Pier Scour Prediction Model for Safe Design and Inspection
Publication: Journal of Bridge Engineering
Volume 20, Issue 6
Abstract
A novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed here. Results from the new approach are compared with existing approaches. Two field datasets from the literature are used in this study. Support vector machine (SVM), which is a machine-learning algorithm, is used to increase the pool of field data samples. For a comprehensive understanding of bridge-pier-scour modeling, a model evaluation function is suggested using an orthogonal projection method on a model performance plot. A fast nondominated sorting genetic algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts. The proposed formulation is compared with two selected empirical models [Hydraulic Engineering Circular No. 18 (HEC-18) and Froehlich equation] and a recently developed data-driven model (gene expression programming model). Results show that the proposed model improves the estimation of critical scour depth compared with the other models.
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Acknowledgments
This project is supported by Grant No. RITARS-12-H-ASU from the DOT Research and Innovative Technology Administration (RITA). The authors acknowledge the guidance and contributions of Mr. Caesar Singh, Program Manager at the DOT and thank Dr. Itty P. Itty, Section Leader, Bridge Hydraulics at the Arizona DOT; Dr. Bing Zhao, Branch Manager, Engineering Application Development and River Mechanics Branch at the Flood Control District of Maricopa County; and Mr. Amir Motamedi, Hydrology/Hydraulics Branch Manager at the Flood Control District of Maricopa County, for valuable suggestions. The views, opinions, findings, and conclusions reflected in this paper are the responsibilities of the authors only and do not represent the official policy or position of the U.S. DOT/RITA or any state or other entity.
References
Arneson, L. A., Zevenbergen, L. W., Lagasse, P. F., and Clopper, P. E. (2012). “Evaluating scour at bridges.” FHWA-HIF-12-003, Federal Highway Administration, Washington, DC.
Azamathulla, H. Md., Ghani, A. A., Zakaria, N. A., and Guven, A. (2010). “Genetic programming to predict bridge pier scour.” J. Hydraul. Eng., 165–169.
Bateni, S. M., Borghei, S. M., and Jeng, D. S. (2007). “Neural network and neuro-fuzzy assessments for scour depth around bridge piers.” Eng. Appl. Artif. Intell., 20(3), 401–414.
Chattopadhyay, A., Das, S., and Coelho, C. K. (2007). “Damage diagnosis using a kernel-based method.” Insight-Non-Destructive Testing Condition Monitor., 49(8), 451–458.
Chattopadhyay, A., Fard, Y. F., Gupta, S., and Papanicolaou, T. (2014). “Multi-level adaptive remote sensing system.” U.S. DOT Research and Innovative Technology Administration, 〈http://aims.engineering.asu.edu/RITA/〉 (Aug. 30, 2012).
Das, S., Chattopadhyay, A., and Srivastava, A. N. (2010). “Classifying induced damage in composite plates using one-class support vector machines.” AIAA J., 48(4), 705–718.
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T. A. M. T. (2002). “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput., 6(2), 182–197.
Froelich, D. C. (1988). “Analysis of onsite measurements of scour at piers.” Proc., 1988 National Conf. on Hydraulic Engineering, ASCE, New York, 534–539.
Gaudio, R., Grimaldi, C., Tafarojnoruz, A., and Calomino, F. (2010). “Comparison of formulae for the prediction of scour depth at piers.” Proc., 1st IAHR European Division Congress, Heriot-Watt Univ., U.K.
Guo, J., Suaznabar, O., Shan, H., and Shen, J. (2012). “Pier scour in clear-water conditions with non-uniform bed materials.” FHWA-HRT-12-022, Federal Highway Administration, Research, Development, and Technology, McLean, VA.
Hsu, C. W., Chang, C. C., and Lin, C. J. (2003). “A practical guide to support vector classification.” Technical Rep., National Taiwan Univ., Taipei, Taiwan, 1–12.
Hunt, B. E. (2009). Monitoring scour critical bridges, Vol. 396, Transportation Research Board, Washington, DC.
Inglis, S. C. (1949). “Maximum depth of scour at heads of guide bands and groynes, pier noses, and downstream bridges.” The behavior and control of rivers and canals, Indian Waterways Experimental Station, Poona, India.
Jain, S. C., and Fischer, E. E. (1979). “Scour around circular bridge piers at high Froude numbers.” FHWA-RD-79-104, Federal Highway Administration, Washington, DC.
Jeng, D. S., Bateni, S. M., and Lockett, E. (2005). “Neural network assessment for scour depth around bridge piers.” Civil Engineering Research Rep. R855, Dept. of Civil Engineering, Univ. of Sydney, Sydney, Australia, 1–89.
Johnson, P. A. (1995). “Comparison of pier-scour equations using field data.” J. Hydraul. Eng., 626–629.
Kannan, S., Baskar, S., McCalley, J. D., and Murugan, P. (2009). “Application of NSGA-II algorithm to generation expansion planning.” IEEE Trans. Power Syst., 24(1), 454–461.
Khan, M., Azamathulla, H. M., Tufail, M., and AbGhani, A. (2012). “Bridge pier scour prediction by gene expression programming.” ICE-Water Manage., 165(9), 481–493.
Konak, A., Coit, D. W., and Smith, A. E. (2006). “Multi-objective optimization using genetic algorithms: A tutorial.” Reliab. Eng. Syst. Saf., 91(9), 992–1007.
Landers, M. N., and Mueller, D. S. (1996). “Evaluation of selected pier-scour equations using field data.” Transportation Research Record 1523, Transportation Research Board, Washington, DC, 186–195.
Melville, B. W., and Sutherland, A. J. (1988). “Design method for local scour at bridge piers.” J. Hydraul. Eng., 1210–1226.
Mohamed, T. A., Noor, M. J., Ghazali, A. H., and Huat, B. B. (2005). “Validation of some bridge pier scour formulae using field and laboratory data.” Am. J. Environ. Sci., 1(2), 119–125.
Nassif, H., Ertekin, A. O., and Davis, J. (2002). Evaluation of bridge scour monitoring methods, U.S. DOT, Federal Highway Administration, Trenton, NJ.
Neill, C. R. (1964). “River-bed scour: A review for bridge engineers.” Technical Publication No. 23, Canadian Good Roads Association, Ottawa.
Pal, M., Singh, N. K., and Tiwari, N. K. (2011). “Support vector regression based modeling of pier scour using field data.” Eng. Appl. Artif. Intell., 24(5), 911–916.
Radchenko, A., Pommerenke, D., Chen, G., Maheshwari, P., Shinde, S., Pilla, V., and Zheng, Y. R. (2013). “Real time bridge scour monitoring with magneto-inductive field coupling.” Volume 8692: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, SPIE, Bellingham, WA, 86922A–86922A-15.
Reed, P., Minsker, B. S., and Goldberg, D. E. (2003). “Simplifying multiobjective optimization: An automated design methodology for the nondominated sorted genetic algorithm-II.” Water Resour. Res., 39(7).
Richardson, E. V., and Davis, S. R. (2001). “Evaluating scour at bridges.” FHWA NHI, 01-001, Federal Highway Administration, Washington, DC.
Richardson, E. V., Harrison, L. J., Richardson, J. R., and Davis, S. R. (1993). “Evaluating scour at bridges.” HEC 18, 2nd Ed., Federal Highway Administration, Washington, DC.
Schölkopf, B., and Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond, MIT Press, Cambridge, MA.
Sheppard, D. M., Melville, B., and Demir, H. (2014). “Evaluation of existing equations for local scour at bridge piers.” J. Hydraul. Eng., 14–23.
Vapnik, V. N. (1999). “An overview of statistical learning theory.” IEEE Trans. Neural Networks,, 10(5), 988–999.
Wardhana, K., and Hadipriono, F. C. (2003). “Analysis of recent bridge failures in the United States.” J. Perform. Constr. Facil., 144–150.
Xiong, W., Cai, C. S., and Kong, X. (2012). “Instrumentation design for bridge scour monitoring using fiber Bragg grating sensors.” Appl. Opt., 51(5), 547–557.
Yu, X., and Yu, X. (2010). “Laboratory evaluation of time-domain reflectometry for bridge scour measurement: Comparison with the ultrasonic method.” Adv. Civ. Eng., Vol. 2010, 508172.
Zarafshan, A., Iranmanesh, A., and Ansari, F. (2012). “Vibration-based method and sensor for monitoring of bridge scour.” J. Bridge Eng., 829–838.
Zitzler, E., Deb, K., and Thiele, L. (2000). “Comparison of multiobjective evolutionary algorithms: Empirical results.” Evol. Comput., 8(2), 173–195.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 20 • Issue 6 • June 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 27, 2013
Accepted: Jul 10, 2014
Published online: Aug 19, 2014
Published in print: Jun 1, 2015
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